AUC (Area Under the ROC Curve), which measures the probability that a positive instance has a higher score than a negative instance, is a well-known performance metric for a scoring function's ranking quality. AUC often comes up as a more appropriate performance metric than accuracy in various applications due to its appealing properties, e.g., insensitivity toward label distributions and costs [1]}, [2]}. On one hand, for class-imbalanced tasks such as disease prediction [3]}, [4]} and rare event detection [5]}, [6]}, [7]}, the label distribution is often highly skewed in the sense that the proportion of the majority classes instances significantly dominates the others. As for a typical instance, in the credit card fraud detection dataset released in Kaggle https://www.kaggle.com/mlg-ulb/creditcardfraud, the fraudulent transactions only account for \(0.172\%\)  of the total records.
Accuracy is often not a good choice in this case since it might ignore the performance from the minority classes which are often more crucial than the majority ones. By contrast, the value of AUC does not rest on the label distribution, making it a natural metric under the class-imbalance scenario. On the other hand, AUC has also been adopted as a standard metric for applications such as ads click-through rate prediction and recommender systems [8]}, [9]}, [10]}, [11]} where pursuing a correct ranking between positive and negative instances is much more critical than label prediction.
